Robust Self-Localization of Wireless Acoustic Sensor Networks

Wireless acoustic sensor networks (WASNs), or the so-called Internet of Audio Things (IoAuT), have attracted increasing attention in the Internet of Things community. As the geometric structure of WASNs is required in audio/speech processing tasks like source localization or acoustic beamforming, automatic self-localization of sensors is necessary. However, most of the existing approaches suffer from poor stability, as their constructed cost functions involve nonconvex programming. To address this issue, we investigate the robust self-localization (or geometry calibration) of WASNs in this article. Specifically, a rough self-localization (RSL) method is first presented based on measurements including Time-Difference-of-Arrivals (TDoAs), direction-of-arrivals (DoA), and energy-rates (ERs), and its closed-form solution is further derived. As ER estimates are sensitive to acoustic environments, the performance of the RSL method is somewhat limited. Therefore, a precise self-localization (PSL) method is then developed by building a weighted (and nonconvex) TDoA-DoA cost function, after regarding the RSL approach as an initialization step. As the RSL offers better initial values compared with existing initialization strategies, the combination of RSL and PSL methods (named as RSL-PSL method) shows stronger robustness and stability. In addition, computational complexity of both RSL and PSL methods is analyzed in detail. Finally, the Cramér-Rao Bound (CRB) of the PSL method is derived to show its theoretical lower bound. The proposed RSL-PSL method outperforms the state-of-the-arts in terms of stability and accuracy, which is confirmed by numerical real-world and simulation experiments.